The ACLIM Repository github.com/kholsman/ACLIM2 is maintained by Kirstin Holsman, Alaska Fisheries Science Center, NOAA Fisheries, Seattle WA. Multiple programs and projects have supported the production and sharing of the suite of Bering10K hindcasts and projections. Last updated: Mar 08, 2021

1. Overview

This repository contains R code and Rdata files for working with netcdf-format data generated from the downscaled ROMSNPZ modeling of the ROMSNPZ Bering Sea Ocean Modeling team; Drs. Hermann, Cheng, Kearney, Pilcher,Ortiz, and Aydin. The code and R resources described in this tutorial are publicly available through the ACLIM2 github repository maintained by Kirstin Holsman as part of NOAA’s ACLIM project for the Bering Sea. See Hollowed et al. 2020 for more information about the ACLIM project

1.1. Resources

We strongly recommend reviewing the following documentation before using the data in order to understand the origin of the indices and their present level of skill and validation, which varies considerably across indices and in space and time:

  • The Bering10K Dataset documentation: A pdf describing the dataset, including full model descriptions, inputs for specific results, and a tutorial for working directly with the ROMS native grid (Level 1 outputs).

  • Bering10K Simulaton Variables: A spreadsheet listing all simulations and the archived output variables associated with each, updated periodically as new simulations are run or new variables are made available.

  • A collection of Bering10K ROMSNPZ model documentation (including the above files) is maintained by Kelly Kearney and will be regularly updated with new documentation and publications.

1.2 Guildlines for use and citation of the data

The data described here are published and publicly available for use, except as explicitly noted. However, for novel uses of the data, it is strongly recommended that you consult with and consider including at least one author from the ROMSNPZ team (Drs. Hermann, Cheng, Kearney, Pilcher, Aydin, Ortiz). There are multiple spatial and temporal caveats that are best described in discussions with the authors of these data and inclusion as co-authors will facilitate appropriate application and interpretation.

TODO: add specific citations back in here

1.2.1. The Bering 10K Model (v. H16) with 10 depth layers:

The H16 model is the original BSIERP era 10 depth layer model with a 10 Km grid. This version was used in ACLIM1.0 to dynamically downscaled 3 global scale general circulation models (GCMs) under two CMIP (Coupled Model Intercomparison Project]) phase 5 representative carbon pathways (RCP): RCP 4.5 or “moderate global carbon mitigation” and RCP 8.5 “high baseline global carbon emissions”. Details of the model and projections can be found in:

  • Hindcast (1979-2012; updated to 2018 during ACLIM 1.0):

    Hermann, A. J., G. A. Gibson, N. A. Bond, E. N. Curchitser, K. Hedstrom, W. Cheng, M. Wang, E. D. Cokelet, P. J. Stabeno, and K. Aydin. 2016. Projected future biophysical states of the Bering Sea. Deep Sea Research Part II: Topical Studies in Oceanography 134:30–47.doi:10.1016/j.dsr2.2015.11.001

  • Projections of the H16 10 layer model using CMIP5 scenarios:

    Hermann, A. J., G. A. Gibson, W. Cheng, I. Ortiz, K. Aydin, M. Wang, A. B. Hollowed, K. K. Holsman, and S. Sathyendranath. 2019. Projected biophysical conditions of the Bering Sea to 2100 under multiple emission scenarios. ICES Journal of Marine Science 76:1280–1304.doi:10.1093/icesjms/fsz043)

1.2.2. The Bering 10K Model (v. K20) with 30 depth layers and other advancements:

The Bering10K model was subsequently updated by Kearney et al. 2020 (30 layer and other NPZ updates) and Pilcher et al .2019 (OA and O2 dynamics) and this version is used for the projections in ACLIM2.0 under the most recent CMIP phase 6.

  • Hindcast (1979-2020 hindcast with OA dynamics used in ACLIM 2.0):

    Kearney, K., A. Hermann, W. Cheng, I. Ortiz, and K. Aydin. 2020. A coupled pelagic-benthic-sympagic biogeochemical model for the Bering Sea: documentation and validation of the BESTNPZ model (v2019.08.23) within a high-resolution regional ocean model. Geoscientific Model Development 13:597–650.

    Pilcher, D. J., D. M. Naiman, J. N. Cross, A. J. Hermann, S. A. Siedlecki, G. A. Gibson, and J. T. Mathis. 2019. Modeled Effect of Coastal Biogeochemical Processes, Climate Variability, and Ocean Acidification on Aragonite Saturation State in the Bering Sea. Frontiers in Marine Science 5:1–18.

  • Projections of the K20 30 layer model using CMIP6 scenarios:

    Hermann et al. in prep
    Cheng et al. in prep
    Kearney et al. in prep
    Pilcher et al. in prep (CMIP5 K20 projections) (ACLIM indices avail by permission only)

2. Installation

2.1 Minimal Install

A minimal R install (for example XXX only) requires installing the ncdf4, devtools libraries (available on CRAN), and thredds R library through its github site:

    install.packages(devtools)
    install.packages(ncdf4)
    devtools::install_github("bocinsky/thredds")

Note that each of these has multiple sub-dependent libraries and may take several minutes to install. The full install below includes installation of these packages, so you don’t need to perform this step if you perform the full install.

2.2 Full install

The full install consists of the full directory structure in the ACLIM2 Repo; this includes a substantial set of resource files including shape files and data for performing Bering Sea spatial analysis in R. This will eventually become a library package, but currently requires manual downloading of the full directory structure from github. The full install may take up to 1GB of disk space (initial download ~12MB).

Option 1: Clone the repository

If you have git installed and can work with it, this is the preferred method as it preserves all directory structure and can aid in future updating. Use this from a terminal command line, not in R, to clone the full ACLIM2 directory and subdirectories:

    git clone https://github.com/kholsman/ACLIM2.git

Option 2: Download the repository

Download the full zip archive directly from the ACLIM2 Repo using this link: https://github.com/kholsman/ACLIM2/archive/main.zip, and unzip its contents while preserving directory structure. Important: if downloading from zip, please rename the root folder from ACLIM2-main (in the zipfile) to ACLIM2 (name used in cloned copies) after unzipping, for consistency in the following examples.

Option 3: Use R to download the repository

This set of commands, run within R, downloads the ACLIM2 repository and unpacks it, with the ACLIM2 directory structrue being located in the specified download_path. This also performs the folder renaming mentioned in Option 2.

    # Specify the download directory
    main_nm       <- "ACLIM2"
    # Note: Edit download_path for preference
    download_path <- path.expand("~/desktop")
    dest_fldr     <- file.path(download_path,main_nm)
    
    url           <- "https://github.com/kholsman/ACLIM2/archive/main.zip"
    dest_file     <- file.path(download_path,paste0(main_nm,".zip"))
    download.file(url=url, destfile=dest_file)
    
    # unzip the .zip file
    setwd(download_path)
    unzip (dest_file, exdir = "./",overwrite = T)
    
    #rename the unzipped folder from ACLIM2-main to ACLIM2
    file.rename(paste0(main_nm,"-main"), main_nm)
    setwd(main_nm)

2.3 Set up envionment and get shapefiles (full install)

The remainder of this tutorial was testing in RStudio. This may work in plain R, but is untested. If you are using RStudio, open ACLIM2.Rproj in Rstudio. If using R, use setwd() to get to the main ACLIM2 directory. Then run:

    # --------------------------------------
    # SETUP WORKSPACE
    # rm(list=ls())
    tmstp  <- format(Sys.time(), "%Y_%m_%d")
    main   <- getwd()  #"~/GitHub_new/ACLIM2
    source("R/make.R")
    # --------------------------------------

The R/make.R command will install missing libraries (including those listed under the minimal install) and download and process multiple shapefiles for geographic analysis, it takes several minutes depending on bandwidth.

3. Get ROMSNPZ data (Step 2)

3.1 Available data sources

There are presently two sources of ROMSNPZ level 2 and level 3 post-processed datasets:

  1. Public web-based ACLIM CMIP5 datasets
  • Level1 : (Empty; data not copied from Mox)
  • Level2 : (full grid, rotated to lat lon from the native ROMSNPZ grid, weekly averages)
    • Bottom 5m : subset of variables from the bottom 5 m of the water column
    • Bottom 5m : subset of variables for the surface 5 m of the water column
    • Integrated: watercolumn integrated averages or totals for various variables
    • Level3: two post-processed datasets
      • ACLIMsurveyrep-x.nc.: Survey replicated (variables “sampled” at the average location and date that each groundfish survey is sampled)(Note that the resampling stations need to be removed before creating bottom temperature maps)
    • ACLIMregion-xnc.:weekly variables averaged for each survey strata (Note that area (km2) weighting should be used to combine values across multiple strata)
  1. ACLIM google drive CMIP6 datasets (embargoed; ACLIM members Only)
  • Level1: (Empty; data not copied from Mox)
  • Level2: (Empty; data not copied from Mox)
  • Level3: two post-processed datasets
    • ACLIMsurveyrep-x.nc.: Survey replicated (variables “sampled” at the average location and date that each groundfish survey is sampled)(Note that the resampling stations need to be removed before creating bottom temperature maps)
    • ACLIMregion-xnc.:weekly variables averaged for each survey strata (Note that area (km2) weighting should be used to combine values across multiple strata)

For all files the general naming convention of the folders is: B10K-[ROMSNPZ version]_[CMIP]_[GCM]_[carbon scenario]. For example, the CMIP5 set of indices was downscaled using the H16 (Hermann et al. 2016) version of the ROMSNPZ. Three models were used to force boundary conditions( MIROC, CESM, and GFDL) under 2 carbon scenarios RCP 8.5 and RCP 4.5. So to see an individual trajectory we might look in the level3 (timeseries indices) folder under B10K-H16_CMIP5_CESM_rcp45, which would be the B10K version H16 of the CMIP5 CESM model under RCP4.5.

3.1.1 Option 1: Public web-based ACLIM data (hindcasts & CMIP5 projections)

The public web-based ACLIM data (hindcasts & CMIP5 projections) option is available for Level3 and Level2 CMIP5 public data, it is not yet available for the embargoed CMIP6 data but through ACLIM2.0 will eventually be used to host that as well.

The ROMSNPZ team has been working with Roland Schweitzer and Peggy Sullivan to develop the ACLIM Live Access Server (LAS) to publicly host the published CMIP5 hindcasts and downscaled projections. This server is in beta testing phase and can be accessed at the following links:

3.1.2 Option 2: (ACLIM members Only) Access CMIP6 (embargoed) L3 data

Public CMIP5 and embargoed CMIP6 Level 3 netcdf (.nc) files are saved in the shared ACLIM data folder (note: Level 2 files are too large for the google drive but are available by request from Kelly Kearney.

IMPORTANT Please note that while the CMIP5 set is now public (Hermann et al. 2019; section 2.2) the CMIP6 suite is under embargo for QAQC and should not be shared outside of the ACLIM group. The ROMSNPZ team (Drs. Hermann, Cheng, Kearney, Pilcher, Adyin) are in the process of synthesizing and publishing the CMIP6 data (goal is spring 2021 for submission), following those publications the data will be made accessible to the public via the PMEL data portal, as is the case for the CMIP5 data and public hindcasts. The ROMSNPZ team has made these runs available to ACLIM2 members in order to accelerate coupling to biological and social and economic models, thus out of professional courtesy please do not publish the data without permission from all ROMSNPZ team members, it is strongly advised that some or multiple ROMSNPZ team members be included as co-authors to ensure proper application and use of the ROMSNPZ data.

For most applications you can use the ACLIM level3 post-processed indices available on the shared ACLIM drive in the root google drive data folder: 00_ACLIM_shared>02_DATA.

The Newest folder is organized by Bering10K version, General Circulation Model (GCM) and carbon scenario, e.g. B10K-H16_CMIP5_CESM_rcp45. Within each folder the following subfolders are:

  • Level1: (Empty; not copied from Mox)
  • Level2: (Empty; not copied from Mox)
  • Level3: 2 files (ACLIMsurveyrep_B10K-x.nc and ACLIMregion_B10K-x.nc )
  1. ACLIMsurveyrep_B10K-x.nc contains summer groundfish trawl “survey replicated” indices (using mean date and lat lon) (Note that the resampling stations need to be removed before creating bottom temperature maps)
  2. ACLIMregion_B10K-x.nc: contains weekly “strata” values (Note that area (km2) weighting should be used to combine values across multiple strata)

There are two folders that need to be copied into the ACLIM2 folder on your computer under `~[YOURPATH]/ACLIM2/Data/in/:

  1. 00_ACLIM_shared>02_DATA>Newest. This folder contains a folder called roms_for_aclim with all the ACLIM Level3 indices for model simulations available to ACLIM members.

  2. 00_ACLIM_shared>02_DATA>Map_layers.zip. This file needs to be unzipped after you download it to your local folder. It contains (large) base maps for the code below including shp_files and geo_tif folders.

Your local ACLIM2/Data directory should look something like this when you are done downloading the data and unzipping it.

3.2 Access and save the data

The below code will extract variables from the Level 2 and Level 3 netcdf files (.nc) and save them as compressed .Rdata files on your local Data/in/Newest/Rdata folder.

3.2.1 Setup up the R worksace

First let’s get the workspace set up, will we step through an example downloading the hindcast and a single projection (CMIP5 MIROC rcp8.5) but you can loop the code below to download the full set of CMIP5 projections.

    # --------------------------------------
    # SETUP WORKSPACE
    # rm(list=ls())
    tmstp  <- format(Sys.time(), "%Y_%m_%d")
    main   <- getwd()  #"~/GitHub_new/ACLIM2
    source("R/make.R")
    # --------------------------------------

Let’s take a look at the available online datasets:

    # preview the datasets on the server:
    url_list <- tds_list_datasets(thredds_url = ACLIM_data_url)
    
    #display the full set of datasets:
    cat(paste(url_list$dataset,"\n"))
## Constants/ 
##  B10K-H16_CMIP5_CESM_BIO_rcp85/ 
##  B10K-H16_CMIP5_CESM_rcp45/ 
##  B10K-H16_CMIP5_CESM_rcp85/ 
##  B10K-H16_CMIP5_GFDL_BIO_rcp85/ 
##  B10K-H16_CMIP5_GFDL_rcp45/ 
##  B10K-H16_CMIP5_GFDL_rcp85/ 
##  B10K-H16_CMIP5_MIROC_rcp45/ 
##  B10K-H16_CMIP5_MIROC_rcp85/ 
##  B10K-H16_CORECFS/ 
##  B10K-K20_CORECFS/ 
##  files/

3.2.2 Download Level 2 data

First we will explore the Level 2 bottom temperature data on the ACLIM Thredds server using the H16 hindcast and the H16 (CMIP5) projection for MIROC under rcp8.5. The first step is to get the data urls:

   # define the simulation to download:
    cmip <- "CMIP5"     # Coupled Model Intercomparison Phase
    GCM  <- "MIROC"     # Global Circulation Model
    rcp  <- "rcp85"     # future carbon scenario
    mod  <- "B10K-H16"  # ROMSNPZ model
    hind <- "CORECFS"   # Hindcast
    
    # define the projection simulation:
    proj  <- paste0(mod,"_",cmip,"_",GCM,"_",rcp)
    hind  <- paste0(mod,"_",hind)
    
    # get the url for the projection and hindcast datasets:
    proj_url       <- url_list[url_list$dataset == paste0(proj,"/"),]$path
    hind_url       <- url_list[url_list$dataset == paste0(hind,"/"),]$path
    
    # preview the projection and hindcast data and data catalogs (Level 1, 2, and 3):
    proj_datasets  <- tds_list_datasets(thredds_url = proj_url)
    hind_datasets  <- tds_list_datasets(thredds_url = hind_url)
    
    # get url for the projection and hindcast Level 2 and Level 3 catalogs
    proj_l2_cat    <- proj_datasets[proj_datasets$dataset == "Level 2/",]$path
    proj_l3_cat    <- proj_datasets[proj_datasets$dataset == "Level 3/",]$path
    hind_l2_cat    <- hind_datasets[hind_datasets$dataset == "Level 2/",]$path
    hind_l3_cat    <- hind_datasets[hind_datasets$dataset == "Level 3/",]$path
    hind_l2_cat
## [1] "https://data.pmel.noaa.gov/aclim/thredds/B10K-H16_CORECFS/Level2.html"

Now that we have the URLs let’s take a look at the available Level2 datasets (currently temperature only, other variables available by request to Kelly Kearney:

  • Bottom 5m : bottom water temperature at 5 meters
  • Surface 5m : surface water temperature in the first 5 meters
  • Integrated : Integrated water column averages for various NPZ variables
    # preview the projection and hindcast Level 2 datasets:
    proj_l2_datasets  <- tds_list_datasets(proj_l2_cat)
    hind_l2_datasets  <- tds_list_datasets(hind_l2_cat)
    proj_l2_datasets$dataset
## [1] "Bottom 5m"  "Surface 5m" "Integrated"
    # get url for bottom temperature:
    proj_l2_BT_url    <- proj_l2_datasets[proj_l2_datasets$dataset == "Bottom 5m",]$path
    hind_l2_BT_url    <- hind_l2_datasets[hind_l2_datasets$dataset == "Bottom 5m",]$path
    proj_l2_BT_url
## [1] "https://data.pmel.noaa.gov/aclim/thredds/B10K-H16_CMIP5_MIROC_rcp85/Level2.html?dataset=B10K-H16_CMIP5_MIROC_rcp85_Level2_bottom5m"

We can’t preview the Level 3 datasets in the same way but they are identical to those in the google drive and include two datasets

  • ACLIMsurveyrep_B10K-H16_CMIP5_CESM_BIO_rcp85.nc : NMFS Groundfish summer NBS and EBS survey replicated values for 60+ variables
  • ACLIMregion_B10K-H16_CMIP5_CESM_BIO_rcp85.nc : weekly strata averages for 60+ variables
    weekly_vars  # list of possible variables in the ACLIMregion_ files 
##  [1] "region_area"          "Ben"                  "DetBen"              
##  [4] "Hsbl"                 "IceNH4"               "IceNO3"              
##  [7] "IcePhL"               "aice"                 "hice"                
## [10] "shflux"               "ssflux"               "Cop_integrated"      
## [13] "Cop_surface5m"        "EupO_integrated"      "EupO_surface5m"      
## [16] "EupS_integrated"      "EupS_surface5m"       "Iron_bottom5m"       
## [19] "Iron_integrated"      "Iron_surface5m"       "Jel_integrated"      
## [22] "Jel_surface5m"        "MZL_integrated"       "MZL_surface5m"       
## [25] "NCaO_integrated"      "NCaO_surface5m"       "NCaS_integrated"     
## [28] "NCaS_surface5m"       "NH4_bottom5m"         "NH4_integrated"      
## [31] "NH4_surface5m"        "NO3_bottom5m"         "NO3_integrated"      
## [34] "NO3_surface5m"        "PhL_integrated"       "PhL_surface5m"       
## [37] "PhS_integrated"       "PhS_surface5m"        "prod_Cop_integrated" 
## [40] "prod_EupO_integrated" "prod_EupS_integrated" "prod_Eup_integrated" 
## [43] "prod_Jel_integrated"  "prod_MZL_integrated"  "prod_NCaO_integrated"
## [46] "prod_NCaS_integrated" "prod_NCa_integrated"  "prod_PhL_integrated" 
## [49] "prod_PhS_integrated"  "salt_surface5m"       "temp_bottom5m"       
## [52] "temp_integrated"      "temp_surface5m"       "uEast_bottom5m"      
## [55] "uEast_surface5m"      "vNorth_bottom5m"      "vNorth_surface5m"    
## [58] "fracbelow0"           "fracbelow1"           "fracbelow2"

Now we can download a subset of the Level2 data (full 10KM Lat Lon re-gridded data), here with an example of sampling on Aug 1 of each year:

   # Currently available Level 2 variables
    dl     <- proj_l2_datasets$dataset  # datasets

   # variable list
   svl <- list(
          'Bottom 5m' = "temp",
          'Surface 5m' = "temp",
          'Integrated' = c("EupS","Cop","NCaS") ) 
       
    
    # preview the variables, timesteps, and lat lon in each dataset:
    l2_info <- scan_l2(ds_list = dl,sim_list = "B10K-H16_CORECFS" )
    
    names(l2_info)
    l2_info[["Bottom 5m"]]$vars
    l2_info[["Surface 5m"]]$vars
    l2_info[["Integrated"]]$vars
    max(l2_info[["Integrated"]]$time_steps)
    l2_info[["Integrated"]]$years
    
    
    # Simulation list:
    # --> --> Tinker:add additional projection scenarios here
    sl <- c(hind, proj)

    # Currently available Level 2 variables
    dl     <- proj_l2_datasets$dataset  # datasets
    
    # variables to pull from each data set
    # --> --> Tinker: try subbing in other Integrated variables 
    # (l2_info[["Integrated"]]$vars) into the third list vector 
    svl <- list(
      'Bottom 5m' = "temp",
      'Surface 5m' = "temp",
      'Integrated' = c("EupS","Cop","NCaS") ) 
   
    
    # Let's sample the model years as close to Aug 1 as the model timesteps run:
    # --> --> Tinker - try a different date
    tr          <- c("-08-1 12:00:00 GMT") 
    
    # grab nc files from the aclim server and convert to rdatafiles with the ID Aug1
    get_l2(
      ID          = "_Aug1",
      ds_list     = dl,
      trIN        = tr,
      sub_varlist = svl,  
      sim_list    = sl  )

3.2.3 Download Level 3 data

Now let’s grab some of the Level 3 data and store it in the Data/in/Newest/Rdata folder. This is comparatively faster because Level 3 files are already post-processed to be in the ACLIM indices format and are relatively small:

    # Simulation list:
    # --> --> Tinker:add additional projection scenarios here
    sl <- c(hind, proj)
    
    # variable list
    # --> --> Tinker:add additional variables to varlist
    vl <- c(
              "temp_bottom5m",    # bottom temperature,
              "NCaS_integrated",  # Large Cop
              "Cop_integrated",   # Small Cop
              "EupS_integrated")  # Shelf  euphausiids
    
    # convert  nc files into a long data.frame for each variable
    # three options are:
    # ------------------------------------
    
    # opt 1: access nc files remotely (fast, less local storage needed)
    get_l3(web_nc = TRUE, download_nc = F,
          varlist = vl,sim_list = sl)
    
    # opt 2:  download nc files then access locallly:
    get_l3(web_nc = TRUE, download_nc = T,
          local_path = file.path(local_fl,"aclim_thredds"),
          varlist = vl,sim_list = sl)
    
     # opt 3:  access existing nc files locally:
    get_l3(web_nc = F, download_nc = F,
          local_path = file.path(local_fl,"aclim_thredds"),
          varlist = vl,sim_list = sl)

3.2.4 ACLIM only: Convert CMIP6 Level 3 .nc –> .Rdata

The final folder structure on your local drive in Data/in/Newest should look something like this.

Now let’s convert the CMIP6 Level3 .nc files to .Rdata files (as in section 3.2.3)

    # preview the available CMIP6 data
    dir(file.path(local_fl,"roms_for_aclim"))
    
    # variable list
    vl        <- c(
                  "temp_bottom5m",
                  "NCaS_integrated", # Large Cop
                  "Cop_integrated",  # Small Cop
                  "EupS_integrated") # Euphausiids

  # define the simulation to download:
    cmip <- "CMIP6"     # Coupled Model Intercomparison Phase
    GCM  <- "MIROC"     # Global Circulation Model
    rcp  <- "ssp585"     # future carbon scenario
    mod  <- "B10K-K20P19"  # ROMSNPZ model
    hind <- "CORECFS"      # Hindcast
    
    # define the projection simulation:
    proj  <- paste0(mod,"_",cmip,"_",GCM,"_",rcp)
    hind  <- paste0("B10K-K20","_",hind)
    sl    <- c(hind, proj)
    
    # opt 3:  convert subset of nc files to rdata files for analysis:
    get_l3(web_nc = F, download_nc = F,
          local_path = file.path(local_fl,"roms_for_aclim"),
          varlist = vl,sim_list = sl)

4. Explore indices & plot the data

4.1. A quick intro to Level 2 and 3 data

KERIM’s text and example here

4.2. Level 3 indices:

Level 3 indices can be used to generate seasonal, monthly, and annual indices (like those reported in Reum et al. 2020), Holsman et al. 2020). In the section below we explore these indices in more detail using R, including using (2) above to generate weekly, monthly, and seasonal indices (e.g. Fall Zooplankton) for use in biological models. In section 3 below we explore these indices in more detail using R, including using (2) above to generate weekly, monthly, and seasonal indices (e.g. Fall Zooplankton) for use in biological models. The following examples show how to analyze and plot the ACLIM indices from the .Rdata files created in the previous step 3.

Please be sure to coordinate with ROMSNPZ modeling team members to ensure data is applied appropriately. If you need access to the raw ROMSNPZ files (netcdf, non-regridded large files located on MOX). Please contact Al Hermann or Kelly Kearney. Please note that while the CMIP5 set is now public (Hermann et al. 2019) the CMIP6 suite is under embargo for QAQC and should not be shared outside of the ACLIM group. See Section 1 above for more detail.

4.1.1 Explore Level 3 data catalog

Once the base files and setup are loaded you can explore the index types. Recall that in each scenario folder there are two indices saved within the Level3 subfolders:

  1. ACLIMsurveyrep_B10K-x.nc contains summer groundfish trawl “survey replicated” indices (using mean date and lat lon) (Note that the resampling stations need to be removed before creating bottom temperature maps)
  2. ACLIMregion_B10K-x.nc: contains weekly “strata” values (Note that area weighting should be used to combine values across multiple strata)

First run the below set of code to set up the workspace:

    # --------------------------------------
    # SETUP WORKSPACE
    tmstp  <- format(Sys.time(), "%Y_%m_%d")
    main   <- getwd()  #"~/GitHub_new/ACLIM2
    source("R/make.R")
    # --------------------------------------
    
    # list of the scenario x GCM downscaled ACLIM indices
    for(k in aclim)
     cat(paste(k,"\n"))
    
    embargoed # not yet public or published
    public    # published runs (CMIP5)
    
    # get some info about a scenario:
    all_info1
    all_info2
   
    # variables in each of the two files:
    srvy_vars
    weekly_vars
  
    #summary tables for variables
    srvy_var_def
    weekly_var_def
    
    # explore stations in the survey replicated data:
    head(station_info)

4.1.2 Level 3: Spatial indices (survey replicated)

Let’s start b exploring the survey replicated values for each variable. Steps 2 and 3 generated the Rdata files that are stored in the ACLIMsurveyrep_B10K-[version_CMIPx_GCM_RCP].Rdata in each corresponding simulation folder.

Survey replicated stations, N and S. Survey replicated stations.

The code segment below will recreate the above figures.Note that if this is the first time through it may take 3-5 mins to load the spatial packages and download the files from the web (first time through only).

   # if load_gis is set to FALSE in R/setup.R (default) 
   # we will need to load the gis layers and packages
   # if this is the first time through this would be a good time
   # to grab a coffee...

   source("R/sub_scripts/load_maps.R")
## Loading required package: sp
## 
## Attaching package: 'raster'
## The following object is masked from 'package:plotly':
## 
##     select
## The following object is masked from 'package:nlme':
## 
##     getData
## The following object is masked from 'package:tidyr':
## 
##     extract
## The following object is masked from 'package:dplyr':
## 
##     select
## The following object is masked from 'package:magrittr':
## 
##     extract
## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0
## 
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
## 
##     map
## rgeos version: 0.5-2, (SVN revision 621)
##  GEOS runtime version: 3.7.2-CAPI-1.11.2 
##  Linking to sp version: 1.3-1 
##  Polygon checking: TRUE
   # first convert the station_info object into a shapefile for mapping:
   station_sf         <- convert2shp(station_info)
   station_sf$stratum <- factor(station_sf$stratum)
   
   # plot the stations:
   p <- plot_stations_basemap(sfIN = station_sf,
                              fillIN = "subregion",
                              colorIN = "subregion") + 
     scale_color_viridis_d(begin = .2,end=.6) +
     scale_fill_viridis_d(begin  = .2,end=.6)
  
   if(update.figs){
     p
     ggsave(file=file.path(main,"Figs/stations_NS.jpg"),width=5,height=5)
    }

   p2 <- plot_stations_basemap(sfIN = station_sf,fillIN = "stratum",colorIN = "stratum") + 
     scale_color_viridis_d() +
     scale_fill_viridis_d()
   
   if(update.figs){
     p2
   ggsave(file=file.path(main,"Figs/stations.jpg"),width=5,height=5)}

5. Hindcasts:

some text here

5.1. Level 3 hindcasts

some text here

5.1.1. Level 3 hindcasts: spatial patterns

5.1.2. Level 3 hindcasts: Weekly strata averages

5.1.2. Level 3 hindcasts: Weekly strata averages

5.1.3. Level 3 hindcasts: Seasonal averages

5.1.4. Level 3 hindcasts: Monthly averages

5.2. Level 2 hindcasts

some text here

5.2.1. Level 2 hindcasts: Custom spatial indices

5.2.2. Level 2 hindcasts: M2 mooring comparison

6. Projections:

The ACLIM project utilizes the full “suite” of Bering10K model hindcasts and projections, summarized in the following table. These represent downscaled models hindcast and projections based whereby boundary conditions of the high resolution Bering10K model are forced by the coarser resolution General Circulation Models (GCM) run under Coupled Model Intercomparison Project (CMIP) phase 5 (5th IPCC Assessment Report) or phase 6 (6th IPCC Assessment Report; “AR”) global carbon mitigation scenarios. Hindcasts are similarly forced at the boundaries from global scale climate reanalysis CORE and CFS products. For full details see the Kearney 2021 Tech. Memo.

Table 1: Summary of ROMSNPZ downscaled model runs

CMIP GCM Scenario Def Years Model Source Status
CORECFS Reanalysis Hindcast 1970 - 2018 H16 IEA/ACLIM Public
CORECFS Reanalysis Hindcast 1970 - 2020 K20 MAPP/IEA/ACLIM Public
5 GFDL RCP 4.5 Med. mitigation 2006 - 2099 H16 ACLIM/FATE Public
5 GFDL RCP 8.5 High baseline 2006 - 2099 H16 ACLIM/FATE Public
5 GFDL RCP 8.5bio* High baseline 2006 - 2099 H16 ACLIM/FATE Public
5 MIROC RCP 4.5 Med. mitigation 2006 - 2099 H16 ACLIM/FATE Public
5 MIROC RCP 8.5 High baseline 2006 - 2099 H16 ACLIM/FATE Public
5 CESM RCP 4.5 Med. mitigation 2006 - 2099 H16 ACLIM/FATE Public
5 CESM RCP 8.5 High baseline 2006 - 2080 H16 ACLIM/FATE Public
5 CESM RCP 8.5bio* High baseline 2006 - 2099 H16 ACLIM/FATE Public
6 CESM SSP585 High baseline 2014 - 2099 K20P19 ACLIM2/RTAP Embargo
6 CESM SSP126 High Mitigation 2014 - 2099 K20P19 ACLIM2/RTAP Embargo
6 CESM Historical Historical 1980 - 2014 K20P19 ACLIM2/RTAP Embargo
6 GFDL SSP585 High baseline 2014 - 2099 K20P19 ACLIM2/RTAP Embargo
6 GFDL SSP126 High Mitigation 2014 - 2099 K20P19 ACLIM2/RTAP Embargo
6 GFDL Historical Historical 1980 - 2014 K20P19 ACLIM2/RTAP Embargo
6 MIROC SSP585 High baseline 2014 - 2099 K20P19 ACLIM2/RTAP Embargo
6 MIROC SSP126 High Mitigation 2014 - 2099 K20P19 ACLIM2/RTAP Embargo
6 MIROC Historical Historical 1980 - 2014 K20P19 ACLIM2/RTAP Embargo

*“bio” = nutrient forcing on boundary conditions

6.1. Level 3 projections

some text here

6.1.1. Level 3 projections: spatial patterns

Now let’s explore the survey replicated data in more detail and use to plot bottom temperature.

    # now create plots of average BT during four time periods
    time_seg   <- list( '2010-2020' = c(2010:2020),
                        '2021-2040' = c(2021:2040),
                        '2041-2060' = c(2041:2060),
                        '2061-2080' = c(2061:2080),
                        '2081-2099' = c(2081:2099))
  
    # View an individual variable (e.g., Bottom Temp)
    # -------------------------------------------------------
    head(srvy_vars)
    head(aclim)
    
    # assign the simulation to download
    # --> --> Tinker: try selecting a different set of models to compare
    sim        <-"B10K-H16_CMIP5_MIROC_rcp85" 
    
    # open a "region" or strata specific nc file
    fl         <- file.path(sim,paste0(srvy_txt,sim,".Rdata"))
    
    # load object 'ACLIMsurveyrep'
    load(file.path(main,Rdata_path,fl))   
     
    # create local rdata files (opt 1)
    if(!file.exists(fl))
      get_l3(web_nc = TRUE, download_nc = F,
          varlist = vl,sim_list =sim )
    
    
     # Collate mean values across timeperiods and simulations
    # -------------------------------------------------------
    m_set      <- c(9,7,8)
    ms         <- aclim[m_set]
   
    
    # Loop over model set
    for(sim in ms){
     fl         <- file.path(sim,paste0(srvy_txt,sim,".Rdata"))
     
    if(!file.exists( file.path(Rdata_path,fl)) )
      get_l3(web_nc = TRUE, download_nc = F,
          varlist = vl,sim_list =sim )
    }
      
    # get the mean values for the time blocks from the rdata versions
    # will throw "implicit NA" errors that can be ignored
    mn_var_all <- get_mn_rd(modset = ms ,varUSE="temp_bottom5m")
    
    # convert results to a shapefile
    mn_var_sf  <- convert2shp(mn_var_all%>%filter(!is.na(mnval)))
    lab_t      <- ms[2]%>%stringr::str_remove("([^-])")
    
    p3         <- plot_stations_basemap(sfIN = mn_var_sf,
                                fillIN = "mnval",
                                colorIN = "mnval",
                                sizeIN=.3) +
      facet_grid(simulation~time_period)+
      scale_color_viridis_c()+
      scale_fill_viridis_c()+
      guides(
        color =  guide_legend(title="Bottom T (degC)"),
        fill  =  guide_legend(title="Bottom T (degC)")) +
      ggtitle(lab_t)
   
    # This is slow but it works (repeat dev.new() twice if in Rstudio)...
    dev.new()
    p3
    
    if(update.figs)  ggsave(file=file.path(main,"Figs/mn_BT.jpg"),width=8,height=6)
  
    # graphics.off()

Bottom temperature projections under differing SSP126 (top row) and SSP585 (bottom row)

6.1.2. Level 3 projections: Weekly strata averages

The next set of indices to will explore are the weekly strata-specific values for each variable.These are stored in the ACLIMregion_B10K-[version_CMIPx_GCM_RCP].nc in each scenario folder.

    # View an individual variable (e.g., Bottom Temp)
    # -------------------------------------------------------
    weekly_vars
    aclim
    sim        <-"B10K-H16_CMIP5_MIROC_rcp85" 
    
    # open a "region" or strata specific nc file
    fl         <- file.path(sim,paste0(reg_txt,sim,".Rdata"))
    
    var_use   <- "temp_bottom5m"
    vl        <- c(
                  "temp_bottom5m",
                  "NCaS_integrated", # Large Cop
                  "Cop_integrated",  # Small Cop
                  "EupS_integrated") # Euphausiids
    
    # create local rdata files (opt 1)
    if(!file.exists(fl))
      get_l3(web_nc = TRUE, download_nc = F,
          varlist = vl,sim_list = sim)
    
    # load object 'ACLIMregion'
    load(file.path(main,Rdata_path,fl))  
    tmp_var    <- ACLIMregion%>%filter(var == var_use)
    
   # now plot the data:
   
   p4 <- ggplot(data = tmp_var) + 
     geom_line(aes(x=time,y=val,color= strata),alpha=.8)+
     facet_grid(basin~.)+
     ylab(tmp_var$units[1])+
     ggtitle( paste(sim,tmp_var$var[1]))+
     theme_minimal()
   p4
    if(update.figs)  ggsave(file=file.path(main,"Figs/weekly_bystrata.jpg"),width=8,height=5)

   
   # To get the average value for a set of strata, weight the val by the area:
   mn_NEBS <- getAVGnSUM(strataIN = NEBS_strata, dataIN = tmp_var)
   mn_NEBS$basin = "NEBS"
   mn_SEBS <-getAVGnSUM(strataIN = SEBS_strata, dataIN = tmp_var)
   mn_SEBS$basin = "SEBS"
   
   p5 <- ggplot(data = rbind(mn_NEBS,mn_SEBS)) + 
      geom_line(aes(x=time,y=mn_val,color=basin),alpha=.8)+
      geom_smooth(aes(x=time,y=mn_val,color=basin),
                  formula = y ~ x, se = T)+
      facet_grid(basin~.)+
      scale_color_viridis_d(begin=.4,end=.8)+
      ylab(tmp_var$units[1])+
      ggtitle( paste(sim,mn_NEBS$var[1]))+
     
      theme_minimal()
  p5
  if(update.figs)  ggsave(file=file.path(main,"Figs/weekly_byreg.jpg"),width=8,height=5)

Weekly indcices by sub-region

Weekly indcices by sub-region ### 6.1.3. Level 3 projections: Seasonal averages

Now using a similar approach get the monthly mean values for a variable:

     sim <-"B10K-H16_CMIP5_MIROC_rcp85" 

    # Set up seasons (this follows Holsman et al. 2020)
      seasons <- data.frame(mo = 1:12, 
                   season =factor("",
                     levels=c("Winter","Spring","Summer","Fall")))
      seasons$season[1:3]   <- "Winter"
      seasons$season[4:6]   <- "Spring"
      seasons$season[7:9]   <- "Summer"
      seasons$season[10:12] <- "Fall"
    
       
    vl <- c(
                  "temp_bottom5m",
                  "NCaS_integrated", # Large Cop
                  "Cop_integrated",  # Small Cop
                  "EupS_integrated") # Euphausiids
    
    # create local rdata files (opt 1)
    if(!file.exists(fl))
      get_l3(web_nc = TRUE, download_nc = F,
          varlist = vl,sim_list = sim)
    
    # open a "region" or strata specific  file
    fl      <- file.path(sim,paste0(reg_txt,sim,".Rdata"))
    load(file.path(main,Rdata_path,fl))
    
    # get large zooplankton as the sum of euph and NCaS
    tmp_var    <- ACLIMregion%>%
      filter(var%in%vl[c(2,3)])%>%
      group_by(time,strata,strata_area_km2,basin)%>%
      group_by(time,
             strata,
             strata_area_km2,
             basin,
             units)%>%
      summarise(val =sum(val))%>%
      mutate(var       = "Zoop_integrated",
             long_name ="Total On-shelf 
             large zooplankton concentration, 
             integrated over depth (NCa, Eup)")
    
    rm(ACLIMregion)
    head(tmp_var)
    
    tmp_var$yr     <- strptime(as.Date(tmp_var$time),
                               format="%Y-%m-%d")$year + 1900
    tmp_var$mo     <- strptime(as.Date(tmp_var$time),
                               format="%Y-%m-%d")$mon  + 1
    tmp_var$jday   <- strptime(as.Date(tmp_var$time),
                               format="%Y-%m-%d")$yday + 1
    tmp_var$season <- seasons[tmp_var$mo,2]
    
    # To get the average value for a set of strata, weight the val by the area: (slow...)
    mn_NEBS_season <- getAVGnSUM(
      strataIN = NEBS_strata,
      dataIN = tmp_var,
      tblock=c("yr","season"))
    mn_NEBS_season$basin = "NEBS"
    mn_SEBS_season <- getAVGnSUM(
      strataIN = SEBS_strata, 
      dataIN = tmp_var,
      tblock=c("yr","season"))
    mn_SEBS_season$basin = "SEBS"
    
   plot_data      <- rbind(mn_NEBS_season,mn_SEBS_season)
    
   # plot Fall values:
   p6 <- ggplot(data = plot_data%>%filter(season=="Fall") ) + 
      geom_line(   aes(x = yr,y = mn_val,color=basin),alpha=.8)+
      geom_smooth( aes(x = yr,y = mn_val,color=basin),
                  formula = y ~ x, se = T)+
      facet_grid(basin~.)+
      scale_color_viridis_d(begin=.4,end=.8)+
      ylab(tmp_var$units[1])+
      ggtitle( paste(sim,"Fall",mn_NEBS_season$var[1]))+
      theme_minimal()
  p6
  
  
  if(update.figs)  
    ggsave(file=file.path(main,"Figs/Fall_large_Zoop.jpg"),width=8,height=5)

Large fall zooplankton integrated concentration ### 6.1.4. Level 3 Projections: Monthly averages Using the same approach we can get monthly averages for a given variable:

    # To get the average value for a set of strata, weight the val by the area: (slow...)
    mn_NEBS_season <- getAVGnSUM(
      strataIN = NEBS_strata,
      dataIN   = tmp_var,
      tblock   = c("yr","mo"))
    mn_NEBS_season$basin = "NEBS"
    mn_SEBS_season <- getAVGnSUM(
      strataIN = SEBS_strata, 
      dataIN = tmp_var,
      tblock=c("yr","mo"))
    mn_SEBS_season$basin = "SEBS"
    
    plot_data      <- rbind(mn_NEBS_season,mn_SEBS_season)
    
   # plot Fall values:
   p7 <- ggplot(data = plot_data%>%filter(mo==9) ) + 
      geom_line(   aes(x = yr,y = mn_val,color=basin),alpha=.8)+
      geom_smooth( aes(x = yr,y = mn_val,color=basin),
                  formula = y ~ x, se = T)+
      facet_grid(basin~.)+
      scale_color_viridis_d(begin=.4,end=.8)+
      ylab(tmp_var$units[1])+
      ggtitle( paste(aclim[2],"Sept.",mn_NEBS_season$var[1]))+
      theme_minimal()
  p7
  
  if(update.figs)  
    ggsave(file=file.path(main,"Figs/Sept_large_Zoop.jpg"),width=8,height=5)

September large zooplankton integrated concentration

Finally we can use this approach to plot the monthly averages and look for phenological shifts:

  # or average in 4 time slices by mo:
  # now create plots of average BT during four time periods
    time_seg   <- list( '2010-2020' = c(2010:2020),
                        '2021-2040' = c(2021:2040),
                        '2041-2060' = c(2041:2060),
                        '2061-2080' = c(2061:2080),
                        '2081-2099' = c(2081:2099))
    
    plot_data$ts <-names(time_seg)[1]
    for(tt in 1:length((time_seg)))
      plot_data$ts[plot_data$yr%in%(time_seg[[tt]][1]:time_seg[[tt]][2])]<-names(time_seg)[tt]
    
    plot_data2 <- plot_data%>%
      group_by(var,mo,units,long_name,basin, ts)%>%
      summarize(mn_val2 = mean(mn_val))
   
  # now plot phenological shift:
   p8 <- ggplot(data = plot_data2 ) + 
      geom_line(   aes(x = mo,y = mn_val2,color=ts),alpha=.8,size=0)+
      geom_smooth( aes(x = mo,y = mn_val2,color=ts),
                  formula = y ~ x, se = F)+
      facet_grid(basin~.)+
      scale_color_viridis_d(begin=.9,end=.2)+
      ylab(tmp_var$units[1])+
      ggtitle( paste(aclim[2],mn_NEBS_season$var[1]))+
      theme_minimal()
  p8
  if(update.figs)  
    ggsave(file=file.path(main,"Figs/PhenShift_large_Zoop.jpg"),width=8,height=5)

September large zooplankton integrated concentration

6.2. Level 2 projections

some text here

6.2.1 Level 2 projections: Custom spatial indices

Level 2 data can be explored in the same way as the above indices but we will focus in the section below on a simple spatial plot and temporal index. The advantage of Level2 inidces is in the spatial resolution and values outside of the survey area.

   # define four time periods
    time_seg   <- list( '2010-2020' = c(2000:2020),
                        '2021-2040' = c(2021:2040),
                        '2041-2060' = c(2041:2060),
                        '2061-2080' = c(2061:2080),
                        '2081-2099' = c(2081:2099))
  
    # View an individual variable (e.g., Bottom Temp)
    # -------------------------------------------------------
    head(srvy_vars)
    head(aclim)
    
    # assign the simulation to download
    # --> --> Tinker: try selecting a different set of models to compare
    sim        <-"B10K-H16_CMIP5_MIROC_rcp85" 
    
    svl <- list(
      'Bottom 5m' = "temp",
      'Surface 5m' = "temp",
      'Integrated' = c("EupS","Cop","NCaS") ) 
   
    # Currently available Level 2 variables
    dl     <- proj_l2_datasets$dataset  # datasets
   
    
    # Let's sample the model years as close to Aug 1 as the model timesteps run:
    tr          <- c("-08-1 12:00:00 GMT") 
    
    # the full grid is large and takes a longtime to plot, so let's subsample the grid every 4 cells
   
    IDin       <- "_Aug1_subgrid"
    var_use    <- "_bottom5m_temp"
    
    # open a "region" or strata specific nc file
    fl         <- file.path(main,Rdata_path,sim,"Level2",paste0(sim,var_use,IDin,".Rdata"))
    
    # load object 'ACLIMsurveyrep'
    if(!file.exists(fl))
      get_l2(
        ID          = IDin,
        xi_rangeIN  = seq(1,182,10),
        eta_rangeIN = seq(1,258,10),
        ds_list     = dl,
        trIN        = tr,
        sub_varlist = svl,  
        sim_list    = sim  )
    
    # load R data file
    load(fl)   # temp
    
    # there are smarter ways to do this;looping because 
    # we don't want to mess it up but this is slow...
    i <-1
    data_long <- data.frame(latitude = as.vector(temp$lat),
                       longitude = as.vector(temp$lon),
                       val = as.vector(temp$val[,,i]),
                       time = temp$time[i],
                       year = substr( temp$time[i],1,4),stringsAsFactors = F
                       )
    for(i in 2:dim(temp$val)[3])
      data_long <- rbind(data_long,
                          data.frame(latitude = as.vector(temp$lat),
                           longitude = as.vector(temp$lon),
                           val = as.vector(temp$val[,,i]),
                           time = temp$time[i],
                           year = substr( temp$time[i],1,4),stringsAsFactors = F)
                       )
    
    
    # get the mean values for the time blocks from the rdata versions
    # will throw "implicit NA" errors that can be ignored
    tmp_var <-data_long # get mean var val for each time segment
    j<-0
    for(i in 1:length(time_seg)){
      if(length( which(as.numeric(tmp_var$year)%in%time_seg[[i]] ))>0){
        j <- j +1
         mn_tmp_var <- tmp_var%>%
          filter(year%in%time_seg[[i]],!is.na(val))%>%
          group_by(latitude, longitude)%>%
          summarise(mnval = mean(val,rm.na=T))
        
        mn_tmp_var$time_period = factor(names(time_seg)[i],levels=names(time_seg))
      if(j == 1) mn_var <- mn_tmp_var
      if(j >  1) mn_var <- rbind(mn_var,mn_tmp_var)
       rm(mn_tmp_var)
      }
    }
    
    # convert results to a shapefile
    L2_sf  <- convert2shp(mn_var%>%filter(!is.na(mnval)))
    
    p9     <- plot_stations_basemap(sfIN = L2_sf,
                                fillIN = "mnval",
                                colorIN = "mnval",
                                sizeIN=.6) +
      facet_grid(.~time_period)+
      scale_color_viridis_c()+
      scale_fill_viridis_c()+
      guides(
        color =  guide_legend(title="Bottom T (degC)"),
        fill  =  guide_legend(title="Bottom T (degC)")) +
      ggtitle(paste(sim,var_use,IDin))
   
    # This is slow but it works (repeat dev.new() twice if in Rstudio)...
    dev.new()
    p9
    
    if(update.figs)  ggsave(file=file.path(main,"Figs/sub_grid_mn_BT_Aug1.jpg"),width=8,height=6)
  
    # graphics.off()

Aug 1 Bottom temperature from Level 2 dataset ### 6.2.2 Level 2 projections: Projections at M2 mooring

7. Funding and acknowledgments (needs updating):

7.1 Acknowledgements suggestion: projections

PLEASE Include a statement like the following one in your acknowledgements section:

This study is part of NOAA’s Alaska Climate Integrated Modeling project (ACLIM) and FATE project XXXX. We would like to that the entire ACLIM team including [add specific names] for feedback and discussions on the broader application of this work. Multiple NOAA National Marine Fisheries programs provided support for ACLIM including Fisheries and the Environment (FATE), Stock Assessment Analytical Methods (SAAM) Science and Technology North Pacific Climate Regimes and Ecosystem Productivity, the Integrated Ecosystem Assessment Program (IEA), the NOAA Economics and Social Analysis Division, NOAA Research Transition Acceleration Program (RTAP), the Alaska Fisheries Science Center (ASFC), the Office of Oceanic and Atmospheric Research (OAR) and the National Marine Fisheries Service (NMFS). The scientific views, opinions, and conclusions expressed herein are solely those of the authors and do not represent the views, opinions, or conclusions of NOAA or the Department of Commerce.

For some of the integrated papers the following maybe should also be added:

Additionally, the International Council for the Exploration of the Sea (ICES) and the North Pacific Marine Science Organization (PICES) provided support for Strategic Initiative for the Study of Climate Impacts on Marine Ecosystems (SI-CCME) workshops, which facilitated development of the ideas presented in this paper. The scientific views, opinions, and conclusions expressed herein are solely those of the authors and do not represent the views, opinions, or conclusions of NOAA, the Department of Commerce, ICES, or PICES.